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<table width="100%" summary="page for rwm5yr"><tr><td>rwm5yr</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>rwm5yr</h2>

<h3>Description</h3>

<p>German health registry for the years 1984-1988. Health 
information for years immediately prior to health reform. 
</p>


<h3>Usage</h3>

<pre>data(rwm5yr)</pre>


<h3>Format</h3>

<p>A data frame with 19,609 observations on the following 17 variables.
</p>

<dl>
<dt><code>id</code></dt><dd><p>patient ID  (1=7028)</p>
</dd>
<dt><code>docvis</code></dt><dd><p>number of visits to doctor during year (0-121)</p>
</dd>
<dt><code>hospvis</code></dt><dd><p>number of days in hospital during year (0-51)</p>
</dd>
<dt><code>year</code></dt><dd><p>year; (categorical: 1984, 1985, 1986, 1987, 1988)</p>
</dd>
<dt><code>edlevel</code></dt><dd><p>educational level (categorical: 1-4)</p>
</dd>
<dt><code>age</code></dt><dd><p>age: 25-64</p>
</dd>
<dt><code>outwork</code></dt><dd><p>out of work=1; 0=working</p>
</dd>
<dt><code>female</code></dt><dd><p>female=1; 0=male</p>
</dd>
<dt><code>married</code></dt><dd><p>married=1; 0=not married</p>
</dd>
<dt><code>kids</code></dt><dd><p>have children=1; no children=0</p>
</dd>
<dt><code>hhninc</code></dt><dd><p>household yearly income in marks (in Marks)</p>
</dd>
<dt><code>educ</code></dt><dd><p>years of formal education (7-18)</p>
</dd>
<dt><code>self</code></dt><dd><p>self-employed=1; not self employed=0</p>
</dd>
<dt><code>edlevel1</code></dt><dd><p>(1/0) not high school graduate</p>
</dd>
<dt><code>edlevel2</code></dt><dd><p>(1/0) high school graduate</p>
</dd>
<dt><code>edlevel3</code></dt><dd><p>(1/0) university/college</p>
</dd> 
<dt><code>edlevel4</code></dt><dd><p>(1/0) graduate school</p>
</dd>
</dl>



<h3>Details</h3>

<p>rwm5yr is saved as a data frame.
Count models typically use docvis as response variable. 0 counts are included
</p>


<h3>Source</h3>

<p>German Health Reform Registry, years pre-reform 1984-1988, in Hilbe and Greene (2007)
</p>


<h3>References</h3>

<p>Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press
Hilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press
Hilbe, J. and W. Greene (2008). Count Response Regression Models, in ed. 
C.R. Rao, J.P Miller, and D.C. Rao, Epidemiology and Medical Statistics, 
Elsevier Handbook of  Statistics Series. London, UK: Elsevier.
</p>


<h3>Examples</h3>

<pre>
library(MASS)
data(rwm5yr)

glmrp &lt;- glm(docvis ~ outwork + female + age + factor(edlevel), family=poisson, data=rwm5yr)
summary(glmrp)
exp(coef(glmrp))

## Not run: 
library(msme)
nb2 &lt;- nbinomial(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
summary(nb2)
exp(coef(nb2)) 

glmrnb &lt;- glm.nb(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
summary(glmrnb)
exp(coef(glmrnb))

## End(Not run)
</pre>


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